UPSC CSE – SYLLABUS: GENERAL STUDIES-3- Awareness in the fields of IT, Space, Computers, robotics, nano-technology, bio-technology and issues relating to intellectual property rights.
ALGORITHM BIAS
Machine learning (ML) is a category of algorithm that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output while updating outputs as new data becomes available.
Machine learning bias, also known as algorithm bias or AI bias, is a phenomenon that occurs when an algorithm produces results that are systematically prejudiced due to erroneous assumptions in the machine learning process.
Algorithm Bias:
- The primary source of algorithmic bias is its training data. An algorithm’s prediction is as good as the data it is fed. A machine learning algorithm is designed to learn from patterns in its source data.
- Sometimes, such data may be polluted due to record-keeping flaws, biased community inputs and historical trends.
- Other sources of bias include insufficient data, correlation without causation and a lack of diversity in the database. The algorithm is encouraged to replicate existing biases and a vicious circle is created.
- Bias can lead algorithms to make unfair decisions by reinforcing systemic discrimination. For example, a predictive policing algorithm used for foretelling future crimes may disproportionately target poor persons.
Solution:
- Possible solutions to algorithmic bias could be legal and organisational. The first step to a legal response would be passing an adequate personal data protection law.
- The draft law of the Srikrishna Committee provides a framework to begin the conversation on algorithmic bias. The right to the logic of automated decisions can be provided to individuals.
- Such a right will have to balance the need for algorithmic transparency with organisational interests.
- Additionally, organisational measures can be pegged to a specific legislation on algorithmic bias. In the interests of transparency, entities ought to shed light on the working of their algorithms.
- Entities relying on evaluative algorithms should have public-facing grievance redressal mechanisms.
- An aggrieved individual or community should be able to challenge the decision. Finally, the use of algorithms by government agencies may require public notice to enable scrutiny
The way forward:
- Considering their pervasiveness, algorithms cannot be allowed to operate as unaccountable black boxes.
- The law in India, as well as companies reaping the benefits of AI, must take note and evolve at a suitable pace.
Source:”Indian Express”.
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